Copyright © 2010 Carlos González et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Hyperspectral imaging is a new emerging

1. Introduction to remotely sensed hyperspectral imaging 2. Context: target and anomaly detection algorithms 3. Parallel implementations 3.1. Data partitioning strategies 3.2. Implementation of target detection algorithms in clusters

for region identification in remotely sensed hyperspectral data sets. Comparative results are presented using Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) data collected over the Salinas Valley in California. Keywords-Image segmentation, Hyperspectral imaging,

An important aspect of spectral image analysis is identification of materials present in the object or scene being imaged. Since multi-spectral or hyper spectral imagery is generally low resolution, it is possible for pixels in the image to contain several materials. A paramount issue in image

works must be obtained from the IEEE. Reprinted from IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 5, Part 2, September, 1999. It is well known that high dimensional image data allows for the separation of classes that are spectrally very similar, i.e., possess nearly equal first

ABSTRACT spatial resolution is about 20 m, and the image covers an area of approximately 125 km2. When the area to be Remotely sensed hyperspectral imagery has vast data covered is larger, the data size increases dramatically. volume, for which data compression is a necessary In addition to high

The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging

The Shannon/Nyquist sampling theorem tells us that in order to not lose information when uniformly sampling a signal we must sample at least two times faster than its bandwidth. In many applications, including digital image and video cameras, the Nyquist rate can be so high that we end up with too

We study the notion of Compressed Sensing (CS) as put forward in [14] and related work [20, 3, 4]. The basic idea behind CS is that a signal or image, unknown but supposed to be compressible by a known transform, (eg. wavelet or Fourier), can be subjected to fewer measurements than the nominal

The sparsity which is implicit in MR images is exploited to significantly undersample k-space. Some MR images such as angiograms are already sparse in the pixel representation; other, more complicated images have a sparse representation in some transform domain–for example, in terms of spatial